Methods in automated glycosaminoglycan tandem mass spectra analysis

Glycosylation is the process by which a glycan is enzymatically attached to a protein, and is one of the most common post-translational modifications in nature. One class of glycans is the glycosaminoglycans (GAGs), which are long, linear polysaccharides that are variably sulfated and make up the glycan portion of proteoglycans (PGs). PGs are located on the cellular surface and in the extracellular matrix (ECM), making them important molecules for cell signaling and ligand binding. The GAG sulfation sequence is a determining factor for the signaling capacity of binding complexes, so accurate determination of the sequence is critical. Historically, GAG sequencing using tandem mass spectrometry (MS2) has been a difficult, manual process; however, with the advent of faster computational techniques and higher-resolution MS2, high-throughput GAG sequencing is within reach. Two steps in the pipeline of biomolecule sequencing using MS2 are discovery and interpretation of spectral peaks. The discovery step traditionally is performed using methods that rely on the concept of averagine, or the average molecular building block for the analyte in question. These methods were developed for protein sequencing, but perform considerably worse on GAG sequences, due to the non-uniform distribution of sulfur atoms along the chain and the relatively high isotope abundance of 34S. The interpretation step traditionally is performed manually, which takes time and introduces potential user error. To combat these problems, I developed GAGfinder, the first GAG-specific MS2 peak finding and annotation software. GAGfinder is described in detail in chapter two. Another step in MS2 sequencing is the determination of the sequence using the found MS2 fragments. For a given GAG composition, there are many possible sequences, and peak finding algorithms such as GAGfinder return a list of the peaks in the MS2 mass spectrum. The many-to-many relationship between sequences and fragments can be represented using a bipartite network, and node-ranking techniques can be employed to generate likelihood scores for possible sequences. I developed a bipartite network-based sequencing tool, GAGrank, based on a bipartite network extension of Google’s PageRank algorithm for ranking websites. GAGrank is described in detail in chapter three

Identification of the Type Eleven Secretion System (T11SS) and Characterization of T11SS-dependent Effector Proteins

Host-associated microbes live in dangerous environments as a result of host immune killing, nutrient provisioning, and physiological conditions. Bacteria have evolved a host of surface and secreted proteins to help interact with this host environment and overcome nutrient limitation. The studies included within this dissertation describe the identification of a novel bacterial secretion system which has evolved to transport these symbiosis mediating proteins. This system, termed the type eleven secretion system (T11SS), is present throughout the Gram negative phylum Proteobacteria, including many human pathogens such as Neisseria meningitidis, Acinetobacter baumanii, Haemophilus haemolyticus, and Proteus vulgaris. Furthermore, these studies describe how novel cargo proteins of this secretion system were identified and characterized using molecular biology and physicochemical techniques. Chapter 1 establishes the importance of nematode model systems in researching symbiosis, highlighting how research in entomopathogenic nematodes identified the first T11S. Chapters 2 and 3 use a T11SS-dependent hemophore named hemophilin and its transporter protein to demonstrate T11SS secretion and its mechanisms of cargo specificity. Chapter 3 also explores the role of hemophilin within the nematode symbiont X. nematophila in surviving heme starvation and facilitating nematode fitness. Chapter 4 demonstrates that the lipidated symbiosis factor NilC is surface exposed by the T11SS NilB and uses a combination of metabolomics, proteomics, and lectin library analysis to describe the role of NilC in colonization. Chapter 5 describes a protocol for bioinformatically controlling genome co-occurrence analyses and utilizes this technique to demonstrate significant co-occurrence of T11SS with metal uptake pathways, single carbon metabolism, and mobile genetic elements. Additionally, this protocol allowed prediction of 141 T11SS-dependent cargo falling into 10 distinct architectures, including never before seen T11SS-dependent adhesins and glycoproteins. Finally, Chapter 6 summarizes our findings and contextualizes how the T11SS plays essential roles in host-microbe association in mutualistic bacteria and pathogenic bacteria alike

Genome-scale metabolic modeling of cyanbacteria: network structure, interactions, reconstruction and dynamics

2016 Fall.Includes bibliographical references.Metabolic network modeling, a field of systems biology and bioengineering, enhances the quantitative predictive understanding of cellular metabolism and thereby assists in the development of model-guided metabolic engineering strategies. Metabolic models use genome-scale network reconstructions, and combine it with mathematical methods for quantitative prediction. Metabolic system reconstructions, contain information on genes, enzymes, reactions, and metabolites, and are converted into two types of networks: (i) gene-enzyme-reaction, and (ii) reaction-metabolite. The former details the links between the genes that are known to code for metabolic enzymes, and the reaction pathways that the enzymes participate in. The latter details the chemical transformation of metabolites, step by step, into biomass and energy. The latter network is transformed into a system of equations and simulated using different methods. Prominent among these are constraint-based methods, especially Flux Balance Analysis, which utilizes linear programming tools to predict intracellular fluxes of single cells. Over the past 25 years, metabolic network modeling has had a range of applications in the fields of model-driven discovery, prediction of cellular phenotypes, analysis of biological network properties, multi-species interactions, engineering of microbes for product synthesis, and studying evolutionary processes. This thesis is concerned with the development and application of metabolic network modeling to cyanobacteria as well as E. coli. Chapter 1 is a brief survey of the past, present, and future of constraint-based modeling using flux balance analysis in systems biology. It includes discussion of (i) formulation, (ii) assumption, (iii) variety, (iv) availability, and (v) future directions in the field of constraint based modeling. Chapter 2, explores the enzyme-reaction networks of metabolic reconstructions belonging to various organisms; and finds that the distribution of the number of reactions an enzyme participates in, i.e. the enzyme-reaction distribution, is surprisingly similar. The role of this distribution in the robustness of the organism is also explored. Chapter 3, applies flux balance analysis on models of E. coli, Synechocystis sp. PCC6803, and C. reinhardtii to understand epistatic interactions between metabolic genes and pathways. We show that epistatic interactions are dependent on the environmental conditions, i.e. carbon source, carbon/oxygen ratio in E. coli, and light intensity in Synechocystis sp. PCC6803 and C. reinhardtii. Cyanobacteria are photosynthetic organisms and have great potential for metabolic engineering to produce commercially important chemicals such as biofuels, pharmaceuticals, and nutraceuticals. Chapter 4 presents our new genome scale reconstruction of the model cyanobacterium, Synechocystis sp. PCC6803, called iCJ816. This reconstruction was analyzed and compared to experimental studies, and used for predicting the capacity of the organism for (i) carbon dioxide remediation, and (ii) production of intracellular chemical species. Chapter 5 uses our new model iCJ816 for dynamic analysis under diurnal growth simulations. We discuss predictions of different optimization schemes, and present a scheme that qualitatively matches observations

Study of the interaction between sialic acid-binding immunoglobulin-type lectins (Siglec) and sialylated glycans for the development of a new generation of immunomodulators.

Glycans and complementary glycan-binding proteins represent essential components in the control of both innate and adaptive immunity. Sialic acids are a family of sugars found on the terminal end of mammalian glycoconjugates; they able to act as marker of self in the immune system, as such residues are absent in most microbes. Sialic acid-binding immunoglobulin-like lectins, or Siglecs, are cell surface receptors that recognize sialic acids and are known to modulate immune responses, influencing almost every cell in the hematopoietic system. Siglecs are involved in events like cell adhesion and signaling, inhibition or regulation of the immune cell activation, all mediated by the interaction with sialylated ligands. Sialic acid-Siglec interactions have been associated with a broad spectrum of diseases, stretching from autoimmunity to neurodegeneration and cancer. Thus, strategies for a rational modulation of the interactions between Siglecs and sialylated glycans in pathophysiological processes exhibit a great therapeutic potential. In this context, the present thesis project aimed at the study of the interaction between Siglecs and their cognate sialic acid containing ligands, to disclose the key recognition events underlining host immune suppression or activation. To this end, a multidisciplinary approach combining advanced technologies as ligand-based NMR techniques, including STD-NMR and tr-NOESY, biophysical binding assays and computational methodologies, such as homology modelling docking and MD simulations, was applied to provide an atomistic depiction of the interaction interfaces between various sialoglycans and their receptors. The described strategy has been employed to characterize the binding features of several receptors of the Siglecs family, namely CD22/Siglec-2, Siglec-10 and Siglec-7. CD22 is a B-cell surface inhibitory protein capable of selectively -(2,6) linked sialylated glycans, thus dampening autoimmune responses against self-antigens. The characterization of complex-type N-glycans by CD22 allowed to describe the conformational behavior of the flexible ligands; the formation of CD22 homo-oligomers on the B-cell surface was also addressed. Furthermore, it was provided a global vision of how the most diffuse neuraminic acid forms of sialylated N-glycans are accomodated in the binding pocket of CD22. Moreover, the elucidation of the binding epitope of a synthetic sialo-mimetic upon CD22 interaction afforded new hints for the design and synthesis of high-affinity ligands of therapeutic relevance against B-cell derived malignancies. Then, the Siglec-10, an inhibitory receptor that recognize 2,3 and -linked sialoglycans was studied, thus providing the first insights of the molecular mechanisms regulating the interaction between Siglec-10 and naturally occurring sialoglycans. After that, Siglec-7, a well-established inhibitory receptor that is primarily located on natural killer where it acts as inhibitor of cancer cells cytotoxicity via sialylated ligands binding, has been characterized in the interplay with the oncogenic pathogen F. nucleatum. Indeed, the presence of sialylated lipopolysaccharide (LPS) on certain F. nucleatum strains, hinted that it may have a significant role at the immune interface. The interaction between Siglec-7 and the O-polysaccharide chain from the LPS of F. nucleatum 10953 strain has been depicted, thus delineating a structural binding model that might contribute to explain the etiological role of F. nucleatum in carcinogenesis. A similar approach was employed to other sialoglycan- related systems, i. e. to dissect the mechanism of sialic acid recognition and hydrolysis by mumps virus hemagglutinin neuraminidase, a viral glycoprotein that plays key roles in virus entry and infection; and to assess the binding of the human macrophage galactose-type lectin (MGL) in the interplay with lipooligosaccharide of E. coli strain R1. In conclusion, the structural and functional characterization of Siglec- sialylated glycans interaction have allowed the analysis, at a molecular level, of crucial feature of 3D complexes, highlighting the molecular determinants involved in recognition and binding events, that will aid for the development or optimization of molecules for therapeutic targeting of the Siglecs

Integrating glycomics, proteomics and glycoproteomics to understand the structural basis for influenza a virus evolution and glycan mediated immune interactions

Glycosylation modulates the range and specificity of interactions among glycoproteins and their binding partners. This is important in influenza A virus (IAV) biology because binding of host immune molecules depends on glycosylation of viral surface proteins such as hemagglutinin (HA). Circulating viruses mutate rapidly in response to pressure from the host immune system. As proteins mutate, the virus glycosylation patterns change. The consequence is that viruses evolve to evade host immune responses, which renders vaccines ineffective. Glycan biosynthesis is a non-template driven process, governed by stoichiometric and steric relationships between the enzymatic machinery for glycosylation and the protein being glycosylated. Consequently, protein glycosylation is heterogeneous, thereby making structural analysis and elucidation of precise biological functions extremely challenging. The lack of structural information has been a limiting factor in understanding the exact mechanisms of glycan-mediated interactions of the IAV with host immune-lectins. Genetic sequencing methods allow prediction of glycosylation sites along the protein backbone but are unable to provide exact phenotypic information regarding site occupancy. Crystallography methods are also unable to determine the glycan structures beyond the core residues due to the flexible nature of carbohydrates. This dissertation centers on the development of chromatography and mass spectrometry methods for characterization of site-specific glycosylation in complex glycoproteins and application of these methods to IAV glycomics and glycoproteomics. We combined the site-specific glycosylation information generated using mass spectrometry with information from biochemical assays and structural modeling studies to identify key glycosylation sites mediating interactions of HA with immune lectin surfactant protein-D (SP-D). We also identified the structural features that control glycan processing at these sites, particularly those involving glycan maturation from high-mannose to complex-type, which, in turn, regulate interactions with SP-D. The work presented in this dissertation contributes significantly to the improvement of analytical and bioinformatics methods in glycan and glycoprotein analysis using mass spectrometry and greatly advances the understanding of the structural features regulating glycan microheterogeneity on HA and its interactions with host immune lectins

Unravelling the Layers of Cell Wall Synthesis and Function in Rice

The plant cell wall is of critical importance to plant growth and survival, functioning in maintaining structural integrity, supporting cell expansion, and acting as the first line of defense in response to biotic and abiotic stresses. The major components of the cell wall are cellulose, hemicelluloses, lignin, and pectin. Recent focus on the transcriptional machinery regulating cell wall biosynthesis in plants has revealed many key transcription factors responsible for orchestrating cell wall deposition. However, many of these TFs act redundantly and work coherently with a suite of TFs to activate the cell wall biosynthetic machinery. Heterologous expression of TFs is an ideal way to characterize the conserved roles of a TF in a pathway. Here, we investigate the process of cell wall formation in rice by overexpression of the heterologous TF AtSHN2 from Arabidopsis, to unravel the process of cell wall biosynthesis in rice. Using Tandem Affinity Purification (TAP) enabled Chromatin Immunoprecipitation studies coupled with genome-wide sequencing analysis, cis-regulatory elements bound by AtSHN2 were identified. It was identified that AtSHN2 can bind to GCC box elements present in the promoter sequence of the downstream MYB TFs and [GA]CAACA[TG][AT] element specific of AP2 TFs. Furthermore, transcriptomic profiling of AtSHN2-TAP rice transgenic lines was performed to identify direct and indirect global targets of AtSHN2. In addition, this dissertation also characterizes the role of OsSHN2, the ortholog of AtSHN2 from rice in cell wall biosynthesis in rice using transcriptomic studies. Taken together, this dissertation seeks to unravel the mechanism of cell wall formation in rice using global genomic changes associated with AtSHN2 overexpression in rice plants

Modelling timing in blood cancers

Dysregulation of biological processes in normal cells can lead to the abnormal growth of tumours. Oncogenesis requires the acquisition of advantageous mutations to expand in a fluctuating environment. Cancer cells gain these genetic and epigenetic alterations at different timing in their development, resulting in the formation of heterogeneous cell populations which interact and compete with each others inside tumours. At later stages, by escaping the immune system and acquiring malignant properties, some cancer cells manage to evade the primary tumour and spread in different organs to form metastases. Hence, tumour development in healthy tissues endure several biological changes whilst progressing and the order between these molecular and cellular events may modify prognosis. This thesis addresses the influence of biological event timing on blood cancer progression and clinical outcomes. It first investigates the therapeutic efficacy of p53 restoration in a lymphoma mouse model. While several therapy schedules are tested, all fail due to resistance emergence. Computational modelling establishes the cell dynamics in these tumours and how to use it to propose alternative treatment strategies. Data availability leads this work to explore the impact of molecular evolution in myeloid malignancies. Notably, one study has found that Myeloproliferative Neoplasms patients with both JAK2 and TET2 mutations have different disease characteristics with distinct mutation order. My analyses identify HOXA9 as a potential prognosis marker and biological switch responsible for patient stratification in these patients and in Acute Myeloid Leukemia. Additionally, a molecular network identifies the hematopoietic regulators involved in the branching evolution of Myeloproliferative Neoplasms. Further investigations of the Acute Myeloid Leukemia data show the possible involvement of APP, a gene associated to Alzheimer disease, in early cell fate commitment in hematopoiesis and in poor survival prognosis in undifferentiated leukemia when lowly expressed. Finally, this thesis examines the regulatory dynamics behind three clusters of Acute Myeloid Leukemia patients with distinct levels of HOXA9 and APP expression. By building a program inferring molecular motifs from biological observations, genes which may interact with HOXA9 and APP are identified.Microsoft Research and the MRC Cancer Unit

Metabolic Network Based Gene Essentiality Analysis

Ph.DDOCTOR OF PHILOSOPH

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u